Machine learning approaches for the optimization of packing densities in granular matter

Baule, Adrian; Kurban, Esma; Liu, Kuang; Makse, Hernán A.

doi:10.1039/d2sm01430k

Soft Matter

2023

DOI: 10.1039/d2sm01430k

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Machine learning approaches for the optimization of packing densities in granular matter

Adrian Baule

Esma Kurban

Kuang Liu

et al.

Abstract: The fundamental question of how densely granular matter can pack and how this density depends on the shape of the constituent particles has been a longstanding scientific problem. Previous work...

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Optimal three-dimensional particle shapes for maximally dense saturated packing

Qian,

2024

The Journal of Chemical Physics

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Saturated packing is a random packing state of particles widely applied in investigating the physicochemical properties of granular materials. Optimizing particle shape to maximize packing density is a crucial challenge in saturated packing research. The known optimal three-dimensional shape is an ellipsoid with a saturated packing density of 0.437 72(51). In this work, we generate saturated packings of three-dimensional asymmetric shapes, including spherocylinders, cones, and tetrahedra, via the random sequential adsorption algorithm and investigate their packing properties. Results show that the optimal shape of asymmetric spherocylinders gives the maximum density of 0.4338(9), while cones achieve a higher value of 0.4398(10). Interestingly, tetrahedra exhibit two distinct optimal shapes with significantly high densities of 0.4789(19) and 0.4769(18), which surpass all previous results in saturated packing. The study of adsorption kinetics reveals that the two optimal shapes of tetrahedra demonstrate notably higher degrees of freedom and faster growth rates of the particle number. The analysis of packing structures via the density pair-correlation function shows that the two optimal shapes of tetrahedra possess faster transitions from local to global packing densities.

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Optimal three-dimensional particle shapes for maximally dense saturated packing

Qian,

2024

The Journal of Chemical Physics

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Machine learning approaches for the optimization of packing densities in granular matter

Abstract: The fundamental question of how densely granular matter can pack and how this density depends on the shape of the constituent particles has been a longstanding scientific problem. Previous work...

Cited by 1 publication

References 48 publications

Optimal three-dimensional particle shapes for maximally dense saturated packing

Optimal three-dimensional particle shapes for maximally dense saturated packing

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